AI-in-the-loop: The future of biomedical visual analytics applications in the era of AI

• URL: http://arxiv.org/abs/2412.15876v1
• Date: Fri, 20 Dec 2024 13:27:24 GMT
• Title: AI-in-the-loop: The future of biomedical visual analytics applications in the era of AI
• Authors: Katja Bühler, Thomas Höllt, Thomas Schulz, Pere-Pau Vázquez,
• Abstract summary: How will massive developments of AI in data analytics shape future data visualizations and visual analytics? What are opportunities, open challenges and threats in the context of an increasingly powerful AI? We highlight the potential of AI to transform biomedical visualization as a research field.
• Score: 3.0942901747200975
• License:
• Abstract: AI is the workhorse of modern data analytics and omnipresent across many sectors. Large Language Models and multi-modal foundation models are today capable of generating code, charts, visualizations, etc. How will these massive developments of AI in data analytics shape future data visualizations and visual analytics workflows? What is the potential of AI to reshape methodology and design of future visual analytics applications? What will be our role as visualization researchers in the future? What are opportunities, open challenges and threats in the context of an increasingly powerful AI? This Visualization Viewpoint discusses these questions in the special context of biomedical data analytics as an example of a domain in which critical decisions are taken based on complex and sensitive data, with high requirements on transparency, efficiency, and reliability. We map recent trends and developments in AI on the elements of interactive visualization and visual analytics workflows and highlight the potential of AI to transform biomedical visualization as a research field. Given that agency and responsibility have to remain with human experts, we argue that it is helpful to keep the focus on human-centered workflows, and to use visual analytics as a tool for integrating ``AI-in-the-loop''. This is in contrast to the more traditional term ``human-in-the-loop'', which focuses on incorporating human expertise into AI-based systems.

Related papers

• Data Analysis in the Era of Generative AI [56.44807642944589]
  This paper explores the potential of AI-powered tools to reshape data analysis, focusing on design considerations and challenges. We explore how the emergence of large language and multimodal models offers new opportunities to enhance various stages of data analysis workflow. We then examine human-centered design principles that facilitate intuitive interactions, build user trust, and streamline the AI-assisted analysis workflow across multiple apps.
  arXiv  Detail & Related papers  (2024-09-27T06:31:03Z)
• Human-Modeling in Sequential Decision-Making: An Analysis through the Lens of Human-Aware AI [20.21053807133341]
  We try to provide an account of what constitutes a human-aware AI system. We see that human-aware AI is a design oriented paradigm, one that focuses on the need for modeling the humans it may interact with.
  arXiv  Detail & Related papers  (2024-05-13T14:17:52Z)
• Position Paper: Agent AI Towards a Holistic Intelligence [53.35971598180146]
  We emphasize developing Agent AI -- an embodied system that integrates large foundation models into agent actions. In this paper, we propose a novel large action model to achieve embodied intelligent behavior, the Agent Foundation Model.
  arXiv  Detail & Related papers  (2024-02-28T16:09:56Z)
• Agent AI: Surveying the Horizons of Multimodal Interaction [83.18367129924997]
  "Agent AI" is a class of interactive systems that can perceive visual stimuli, language inputs, and other environmentally-grounded data. We envision a future where people can easily create any virtual reality or simulated scene and interact with agents embodied within the virtual environment.
  arXiv  Detail & Related papers  (2024-01-07T19:11:18Z)
• Can AI Serve as a Substitute for Human Subjects in Software Engineering Research? [24.39463126056733]
  This vision paper proposes a novel approach to qualitative data collection in software engineering research by harnessing the capabilities of artificial intelligence (AI) We explore the potential of AI-generated synthetic text as an alternative source of qualitative data. We discuss the prospective development of new foundation models aimed at emulating human behavior in observational studies and user evaluations.
  arXiv  Detail & Related papers  (2023-11-18T14:05:52Z)
• AI-Generated Images as Data Source: The Dawn of Synthetic Era [61.879821573066216]
  generative AI has unlocked the potential to create synthetic images that closely resemble real-world photographs. This paper explores the innovative concept of harnessing these AI-generated images as new data sources. In contrast to real data, AI-generated data exhibit remarkable advantages, including unmatched abundance and scalability.
  arXiv  Detail & Related papers  (2023-10-03T06:55:19Z)
• AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges [60.56413461109281]
  Artificial Intelligence for IT operations (AIOps) aims to combine the power of AI with the big data generated by IT Operations processes. We discuss in depth the key types of data emitted by IT Operations activities, the scale and challenges in analyzing them, and where they can be helpful. We categorize the key AIOps tasks as - incident detection, failure prediction, root cause analysis and automated actions.
  arXiv  Detail & Related papers  (2023-04-10T15:38:12Z)
• Visual Knowledge Discovery with Artificial Intelligence: Challenges and Future Directions [5.754786889790288]
  Integrated Visual Knowledge Discovery combines advances in Artificial Intelligence/Machine Learning (AI/ML) and visualization. Chapters included are extended versions of the selected AI and Visual Analytics papers and related symposiums. We aim to present challenges and future directions within the field of Visual Analytics, Visual Knowledge Discovery and AI/ML, and to discuss the role of visualization in visual AI/ML.
  arXiv  Detail & Related papers  (2022-05-03T04:17:21Z)
• DIME: Fine-grained Interpretations of Multimodal Models via Disentangled Local Explanations [119.1953397679783]
  We focus on advancing the state-of-the-art in interpreting multimodal models. Our proposed approach, DIME, enables accurate and fine-grained analysis of multimodal models.
  arXiv  Detail & Related papers  (2022-03-03T20:52:47Z)
• Human in the Loop for Machine Creativity [0.0]
  We conceptualize existing and future human-in-the-loop (HITL) approaches for creative applications. We examine and speculate on long term implications for models, interfaces, and machine creativity. We envision multimodal HITL processes, where texts, visuals, sounds, and other information are coupled together, with automated analysis of humans and environments.
  arXiv  Detail & Related papers  (2021-10-07T15:42:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.